Seminar: Sensor Data Compression and Power Management for Activity Recognition

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The aim of the project is to design an energy-efficient context-aware strategy for activity recognition using sparse sampling and sensor data compression. Prototyping will be done in Python. Evaluation will be performed on free-living dataset and benchmarked in terms of energy/memory savings, time constraints and quality of information retrieved. Machine learning techniques will be applied for pattern recognition.

Learning Objectives

  • Gain overview on the state-of-the-art of software-based sparse sampling and signal compression for wearable devices.
  • Learn concepts of data compression.
  • Learn concepts of sparse sampling.

Course data

ECTS 2.5, 5, 7.5, default: 5
Project type Seminar, Extension to BSc.MSc.-Thesis can be discussed
Language English
Presence time 4 SWS
Work distribution 25% Theory, 75% Programming
Useful knowledge Python programming, machine learning basics
Period Summer semester 2019
First meeting Seminar introduction/Vorbesprechung on
24. Apr 2019, 17:00-18:30 at Henkestr. 91, Haus 7, 1. OG, R 373
Med. Eng. Seminar Title Advanced Context Recognition (ACR)


Giovanni Schiboni

Friedrich-Alexander-Universität Erlangen-Nürnberg